1. Technical Field
The present invention relates to an iterative method and associated algorithm for performing image registration to determine a set of transformation parameters that maps features in a first image to corresponding features in a second image in accordance with a transformation model that depends on the transformation parameters.
2. Related Art
Images of the retina are used to diagnose and monitor the progress of a variety of diseases, including such leading causes of blindness as diabetic retinopathy, age-related macular degeneration, and glaucoma. These images, as illustrated in FIGS. 1A–1C in accordance with the related art, are usually acquired by fundus cameras looking through the lens of the eye, and are taken in both the visible spectrum and, using fluorescein and indocyanine green angiography, in the near infrared. FIG. 1A shows a red-free image of a patient's retina, and FIG. 1B shows the red-free image of the patient's retina 3 years later. Differences between the images in FIGS. 1A and 1B are caused by the progression of non-exudative Age-related Macular Degeneration (AMD). The image in FIG. 1C is a fluorescien angiogram image of the same patient taken on the same date as FIG. 1B.
A variety of imaging protocols are used to produce images showing various parts of the retina. Angiography sequences reveal the flow of blood through the retina and are therefore used to highlight blockages and weak, leaking vessels.
Retinal image registration has a variety of applications as shown in FIG. 2, in accordance with the related art. In FIG. 2, the top image pair shows cropped regions of an aligned fluorescein angiogram (left) and red-free image (right). On the angiogram, an ophthalmologist has highlighted leakage of the fluorescein dye. The same region is automatically highlighted on the red-free image, which shows what the ophthalmologist sees when looking through the lens of the eye at the retina. The bottom panel in FIG. 2 shows the alignment of two images of a patient having exudative AMD. The images in FIG. 2 were taken 2.5 years apart. Shifts in the position of the vasculature of more than 4 pixels are highlighted with line segments.
Registering a set of images taken during a single session with a patient can be used to form a single, composite (mosaic) view of the entire retina. Multimodal registration can reveal the relationship between events seen on the surface of the retina and the blood flow shown in the angiography. Registering images taken weeks, months or years apart can be used to reveal changes in the retina at the level of small regions and individual blood vessels.
Retinal image registration is challenging. The images are projections of a curved surface taken from a wide range of viewpoints using an uncalibrated camera. The non-vascular surface of the retina is homogeneous in healthy retinas, and exhibits a variety of pathologies in unhealthy retinas. Unfortunately (for the purposes of registration), these pathologies can appear and disappear over time, making them poor choices for longitudinal registration. Only the vasculature covers the entire retina and is relatively stable over time.
Thus, it appears that a solution to the retinal image registration problem requires an approach driven by the vascular structure. This can include both the vessels themselves and their branching and cross-over points. Choosing to use the vasculature does not make the problem easy, however. There are many vessels and many of these locally appear similar to each other. The effects of disease and poor image quality can obscure the vasculature. Moreover, in different stages of an angiography sequence, different blood vessels can be bright, while others are dark. Finally, the range of viewpoints dictated by some imaging protocols implies the need to register image pairs having small amounts of overlap. Together, these observations imply that (1) initialization is important, (2) minimization will require avoiding local minima caused by misalignments between vessels, and (3) minimization must also be robust to missing structures. These problems are common to many registration problems. Thus, there is a need for a retinal image registration method and associated algorithm that addresses these problems.